基于多传感器信息集成的机器人障碍物检测
王中立，牛颖(太原理工大学信息工程学院，山西太原030024)
摘要：针对机器人避障过程中单传感器不能全面准确定位障碍物的缺点，提出了基于多传感器信息集成的障碍物检测方法。第三阶段，使用视觉传感器检测未知环境中的障碍物Zernike 提取障碍物图像边缘，然后采用矩边检测方法Hough 改变原理提取障碍物的直线特征，获得障碍物的大致位置；在第二阶段，使用超声波传感器和红外传感器检测障碍物，获得障碍物的准确位置；最后，使用联合卡尔曼滤波器整合三个传感器获得的信息，获得集成的障碍物位置信息。三个实验结果表明，该方法克服了视觉传感器、超声波传感器和红外传感器的局限性，能够准确地感知机器人周围未知的环境信息，并能检测和定位机器人路径上的障碍物，定位误差<6cm,三是满足机器人避障的实时性和可靠性要求
关键词:障碍物检测；信息融合；联合卡尔曼滤波；视觉传感器；Zernike 矩；Hough 变换
文献标志码：A 1674-5124(2017)08-0080-060
Obstacle detection of robot based on multi-sensor information fusion
WANG Zhongli,NIU Ying (College of Information Engineering,Taiyuan University of Technology,Taiyuan 030024,China)
Abstract:Aiming at the shortcoming that the single sensor could not locate the obstacle completely and accurately in the process of obstacle avoidance of the robot,an obstacle detection method based on multi-sensor information fusion was proposed.Firstly,vision sensor was used to detect the obstacle in unknown environment.The edge of the obstacle image was extracted by the Zernike moment edge detection method,then the Hough transform principle was used to extract the straight line feature of the obstacle,so as to obtain the approximate position of the obstacle.Secondly,ultrasonic sensor and infrared sensor were used to detect the obstacle to obtain the exact position of obstacles.Finally,the federated Kalman filter was used to fuse the information obtained by the three sensors to gain information of the obstacle position after fusion.The test result shows that this method can overcome the limitations of vision sensors,ultrasonic sensors and infrared sensors,and can accurately detect the unknown environmental information around the robot and detect and locate the obstacles on the path of robot with positioning error less than 6cm,meeting the real-time and reliability of robot obstacle avoidance.Keywords:obstacle detection；information fusion；federated Kalman filter；vision sensor；Zernike moment；Hough transform
收稿日期:2017-03-13；收到修改稿的日期:2017-04-21
作者简介:王中立(1989-)，男，山东菏泽人，硕士，专业方向为检测技术和智能仪器三中国测试CHINA MEASUREMENT &TEST Vol.43No.8August ，2017
2017年8月第43卷doi ：
10.11857/j.issn.1674-5124.2017.08.017